Top Artificial Intelligence Jobs

The AI Engineer will design, implement, test, and maintain high-performance AI and machine learning models, optimizing them for deployment, collaborating with cross-functional teams, and ensuring software quality.

Lead enterprise-wide AI transformation initiatives for Fortune 500 clients, managing complex projects and developing strategies for AI adoption, ethics, and governance.

The role involves developing AI-driven cybersecurity solutions, designing ML models for threat detection, and enhancing security frameworks. Candidates collaborate with SOC teams and assess AI system security.

Design next-gen semiconductors for AI/ML applications, leading advanced development processes and optimizing hardware-software integration.

The AI Implementation Specialist analyzes client workflows, identifies automation opportunities, designs and implements AI-driven solutions to enhance business efficiency and productivity.

The AI Platform Engineer will design and implement MLOps pipelines, develop automated CI/CD systems, and optimize ML infrastructure for performance, cost, and reliability.

Oversee the development and commercialization of AI products, lead teams, conduct market research, and manage product lifecycle.

The AI DevOps Engineer will design, deploy, and manage scalable AI infrastructure, focusing on automation, security, and high performance while ensuring compliance within financial services.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

The Data Analyst will analyze data using Google BigQuery and Generative AI, optimize queries, create visualizations, and ensure data quality for strategic decisions.

Lead research and implementation of large-scale, multi-task foundation models for dexterous robotic manipulation; manage the data flywheel from specification through collection, training, and real-world evaluation; fine-tune and specialize models; write maintainable Python deep-learning code; and collaborate with Robotics and Hardware teams to deploy models on real robots while co-leading AI engineering efforts.

Lead development of large-scale, multi-task robotic foundation models and manage the data flywheel: specification, collection, training, and real-world evaluation. Implement model specialization and fine-tuning, write production-quality Python for deep learning, and collaborate with robotics and hardware teams to deploy on real robots.

Lead signal integrity analysis for photonic-electronic systems, collaborating with design teams to optimize high-speed data paths and package stack-ups.

The Senior Engineering Manager will lead engineering teams in a high-growth environment, prioritize tasks with Product, and coach developers.

Seeking an AI Research Engineer to design AI training protocols, build scalable systems, and collaborate on novel AI infrastructure solutions.

Gradient Network seeks an AI Researcher to conduct original research in distributed ML, model integrity, and incentive design, working with engineering teams and engaging with the global research community.

Contribute to AI-driven healthcare solutions by designing and building AI-centric features, integrating LLMs, and ensuring system reliability. Collaborate effectively and take ownership of projects in a fast-paced environment.

As an AI Research Scientist, you will conduct original research in learning-based robotics, working collaboratively with global teams to deploy solutions on actual hardware.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Lead presales solutioning for Data and AI opportunities using Snowflake, design architectures, conduct workshops, and drive AI-infused data solutions.

Design and develop AI systems for personalized travel and concierge services, focusing on integration and optimization of various technologies.

As a Senior Hardware Systems Engineer, you will lead AI rack and cluster infrastructure designs, manage deployments, and collaborate with cross-functional teams to optimize data center solutions.

Design and implement AI-driven test automation and performance engineering for cloud-native, distributed systems. Build CI/CD-integrated automation frameworks, use ML/LLM techniques for test generation and prioritization, establish performance baselines and observability-driven validation, and mentor teams within SAFe delivery trains to embed quality across the SDLC.

Architect and lead the enterprise AI platform roadmap, building scalable developer environments, multi-modal AI pipelines, agent orchestration, vector-data solutions, RAG/cognitive search capabilities, vendor ecosystems, and guardrails for model observability, drift detection, security, and responsible AI. Collaborate cross-functionally to accelerate adoption and embed AI across the enterprise.